Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations2823
Missing cells34473
Missing cells (%)48.8%
Total size in memory551.5 KiB
Average record size in memory200.0 B

Variable types

Text17
Numeric8

Alerts

QUANTITYORDERED has 1315 (46.6%) missing valuesMissing
PRICEEACH has 1315 (46.6%) missing valuesMissing
ORDERLINENUMBER has 1315 (46.6%) missing valuesMissing
SALES has 1315 (46.6%) missing valuesMissing
ORDERDATE has 1315 (46.6%) missing valuesMissing
STATUS has 1315 (46.6%) missing valuesMissing
QTR_ID has 1315 (46.6%) missing valuesMissing
MONTH_ID has 1315 (46.6%) missing valuesMissing
YEAR_ID has 1315 (46.6%) missing valuesMissing
PRODUCTLINE has 1315 (46.6%) missing valuesMissing
MSRP has 1315 (46.6%) missing valuesMissing
PRODUCTCODE has 1315 (46.6%) missing valuesMissing
CUSTOMERNAME has 1315 (46.6%) missing valuesMissing
PHONE has 1315 (46.6%) missing valuesMissing
ADDRESSLINE1 has 1315 (46.6%) missing valuesMissing
ADDRESSLINE2 has 2684 (95.1%) missing valuesMissing
CITY has 1315 (46.6%) missing valuesMissing
STATE has 1784 (63.2%) missing valuesMissing
POSTALCODE has 1377 (48.8%) missing valuesMissing
COUNTRY has 1315 (46.6%) missing valuesMissing
TERRITORY has 2328 (82.5%) missing valuesMissing
CONTACTLASTNAME has 1315 (46.6%) missing valuesMissing
CONTACTFIRSTNAME has 1315 (46.6%) missing valuesMissing
DEALSIZE has 1315 (46.6%) missing valuesMissing

Reproduction

Analysis started2025-11-10 21:23:52.691110
Analysis finished2025-11-10 21:23:53.883268
Duration1.19 second
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct1480
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:54.225698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length233
Median length5
Mean length92.10556146
Min length5

Characters and Unicode

Total characters260014
Distinct characters76
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1321 ?
Unique (%)46.8%

Sample

1st row10107
2nd row10121
3rd row10134
4th row10145
5th row10159
ValueCountFrequency (%)
555306
 
2.2%
moralzarzal259
 
1.8%
94259
 
1.8%
channel,(91259
 
1.8%
shopping259
 
1.8%
44,"c259
 
1.8%
and182
 
1.3%
rue153
 
1.1%
86",,madrid,,28034,spain,emea,freyre,diego,medium131
 
0.9%
collectables124
 
0.9%
Other values (2997)11986
84.5%
2025-11-10T21:23:54.736378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,33073
 
12.7%
018666
 
7.2%
113716
 
5.3%
211569
 
4.4%
11402
 
4.4%
e10675
 
4.1%
a10166
 
3.9%
58334
 
3.2%
48331
 
3.2%
i7988
 
3.1%
Other values (66)126094
48.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter91115
35.0%
Decimal Number84922
32.7%
Other Punctuation43793
16.8%
Uppercase Letter25400
 
9.8%
Space Separator11402
 
4.4%
Connector Punctuation1315
 
0.5%
Dash Punctuation674
 
0.3%
Close Punctuation531
 
0.2%
Open Punctuation531
 
0.2%
Math Symbol289
 
0.1%
Other values (2)42
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10675
11.7%
a10166
11.2%
i7988
 
8.8%
r7562
 
8.3%
n6616
 
7.3%
l6214
 
6.8%
o5725
 
6.3%
s5722
 
6.3%
d4385
 
4.8%
p4123
 
4.5%
Other values (16)21939
24.1%
Uppercase Letter
ValueCountFrequency (%)
S5163
20.3%
C3386
13.3%
M3080
12.1%
E2525
9.9%
A2441
9.6%
L988
 
3.9%
F947
 
3.7%
D775
 
3.1%
P752
 
3.0%
T677
 
2.7%
Other values (14)4666
18.4%
Decimal Number
ValueCountFrequency (%)
018666
22.0%
113716
16.2%
211569
13.6%
58334
9.8%
48331
9.8%
37221
 
8.5%
84807
 
5.7%
64534
 
5.3%
94121
 
4.9%
73623
 
4.3%
Other Punctuation
ValueCountFrequency (%)
,33073
75.5%
.3175
 
7.3%
"2986
 
6.8%
/2979
 
6.8%
:1315
 
3.0%
'174
 
0.4%
&59
 
0.1%
¡32
 
0.1%
Space Separator
ValueCountFrequency (%)
11402
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1315
100.0%
Dash Punctuation
ValueCountFrequency (%)
-674
100.0%
Close Punctuation
ValueCountFrequency (%)
)531
100.0%
Open Punctuation
ValueCountFrequency (%)
(531
100.0%
Math Symbol
ValueCountFrequency (%)
+289
100.0%
Currency Symbol
ValueCountFrequency (%)
¤23
100.0%
Control
ValueCountFrequency (%)
„19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common143499
55.2%
Latin116515
44.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10675
 
9.2%
a10166
 
8.7%
i7988
 
6.9%
r7562
 
6.5%
n6616
 
5.7%
l6214
 
5.3%
o5725
 
4.9%
s5722
 
4.9%
S5163
 
4.4%
d4385
 
3.8%
Other values (40)46299
39.7%
Common
ValueCountFrequency (%)
,33073
23.0%
018666
13.0%
113716
9.6%
211569
 
8.1%
11402
 
7.9%
58334
 
5.8%
48331
 
5.8%
37221
 
5.0%
84807
 
3.3%
64534
 
3.2%
Other values (16)21846
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII259940
> 99.9%
None74
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,33073
 
12.7%
018666
 
7.2%
113716
 
5.3%
211569
 
4.5%
11402
 
4.4%
e10675
 
4.1%
a10166
 
3.9%
58334
 
3.2%
48331
 
3.2%
i7988
 
3.1%
Other values (63)126020
48.5%
None
ValueCountFrequency (%)
¡32
43.2%
¤23
31.1%
„19
25.7%

QUANTITYORDERED
Real number (ℝ)

Missing 

Distinct56
Distinct (%)3.7%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean35.13793103
Minimum6
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:54.860308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21
Q127
median35
Q343
95-th percentile49
Maximum85
Range79
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.730179021
Coefficient of variation (CV)0.2769138288
Kurtosis0.2701094943
Mean35.13793103
Median Absolute Deviation (MAD)8
Skewness0.3283178087
Sum52988
Variance94.67638377
MonotonicityNot monotonic
2025-11-10T21:23:54.998727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3460
 
2.1%
4860
 
2.1%
2758
 
2.1%
3657
 
2.0%
2655
 
1.9%
3155
 
1.9%
3354
 
1.9%
4652
 
1.8%
2952
 
1.8%
2552
 
1.8%
Other values (46)953
33.8%
(Missing)1315
46.6%
ValueCountFrequency (%)
62
0.1%
102
0.1%
111
< 0.1%
121
< 0.1%
131
< 0.1%
ValueCountFrequency (%)
851
< 0.1%
771
< 0.1%
762
0.1%
701
< 0.1%
661
< 0.1%

PRICEEACH
Real number (ℝ)

Missing 

Distinct633
Distinct (%)42.0%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean83.95025862
Minimum27.22
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:55.142665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27.22
5-th percentile42.26
Q169.87
median96.7
Q3100
95-th percentile100
Maximum100
Range72.78
Interquartile range (IQR)30.13

Descriptive statistics

Standard deviation20.12215198
Coefficient of variation (CV)0.2396913637
Kurtosis-0.2399313702
Mean83.95025862
Median Absolute Deviation (MAD)3.3
Skewness-0.9936937114
Sum126596.99
Variance404.9010001
MonotonicityNot monotonic
2025-11-10T21:23:55.296918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100708
25.1%
90.574
 
0.1%
51.934
 
0.1%
53.724
 
0.1%
33.234
 
0.1%
89.384
 
0.1%
644
 
0.1%
61.993
 
0.1%
88.633
 
0.1%
76.433
 
0.1%
Other values (623)767
27.2%
(Missing)1315
46.6%
ValueCountFrequency (%)
27.221
 
< 0.1%
28.291
 
< 0.1%
28.881
 
< 0.1%
29.543
0.1%
29.71
 
< 0.1%
ValueCountFrequency (%)
100708
25.1%
99.911
 
< 0.1%
99.661
 
< 0.1%
99.581
 
< 0.1%
99.552
 
0.1%

ORDERLINENUMBER
Real number (ℝ)

Missing 

Distinct18
Distinct (%)1.2%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean6.429708223
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:55.419095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile14
Maximum18
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.230388145
Coefficient of variation (CV)0.6579440308
Kurtosis-0.5547808912
Mean6.429708223
Median Absolute Deviation (MAD)3
Skewness0.6049765433
Sum9696
Variance17.89618386
MonotonicityNot monotonic
2025-11-10T21:23:55.521479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1165
 
5.8%
2159
 
5.6%
3147
 
5.2%
4138
 
4.9%
5127
 
4.5%
6117
 
4.1%
7105
 
3.7%
8100
 
3.5%
986
 
3.0%
1073
 
2.6%
Other values (8)291
 
10.3%
(Missing)1315
46.6%
ValueCountFrequency (%)
1165
5.8%
2159
5.6%
3147
5.2%
4138
4.9%
5127
4.5%
ValueCountFrequency (%)
186
 
0.2%
1711
 
0.4%
1623
0.8%
1530
1.1%
1446
1.6%

SALES
Real number (ℝ)

Missing 

Distinct1490
Distinct (%)98.8%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean3568.921194
Minimum541.14
Maximum14082.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:55.661448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum541.14
5-th percentile1263.731
Q12216.58
median3194.395
Q34508.27
95-th percentile7164.107
Maximum14082.8
Range13541.66
Interquartile range (IQR)2291.69

Descriptive statistics

Standard deviation1847.735232
Coefficient of variation (CV)0.5177293449
Kurtosis1.859279439
Mean3568.921194
Median Absolute Deviation (MAD)1094.285
Skewness1.168804506
Sum5381933.16
Variance3414125.486
MonotonicityNot monotonic
2025-11-10T21:23:55.802383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26242
 
0.1%
34512
 
0.1%
30032
 
0.1%
5464.692
 
0.1%
37342
 
0.1%
3508.82
 
0.1%
2803.22
 
0.1%
26882
 
0.1%
1459.62
 
0.1%
3789.722
 
0.1%
Other values (1480)1488
52.7%
(Missing)1315
46.6%
ValueCountFrequency (%)
541.141
< 0.1%
577.61
< 0.1%
640.051
< 0.1%
651.81
< 0.1%
717.41
< 0.1%
ValueCountFrequency (%)
14082.81
< 0.1%
12536.51
< 0.1%
11623.71
< 0.1%
11336.71
< 0.1%
11279.21
< 0.1%

ORDERDATE
Text

Missing 

Distinct146
Distinct (%)9.7%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:23:55.986962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length14.01061008
Min length13

Characters and Unicode

Total characters21128
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row2/24/2003 0:00
2nd row5/7/2003 0:00
3rd row7/1/2003 0:00
4th row8/25/2003 0:00
5th row10/10/2003 0:00
ValueCountFrequency (%)
0:001508
50.0%
11/4/200429
 
1.0%
11/5/200328
 
0.9%
9/8/200426
 
0.9%
10/22/200426
 
0.9%
11/5/200423
 
0.8%
2/17/200522
 
0.7%
10/15/200422
 
0.7%
7/21/200420
 
0.7%
10/10/200319
 
0.6%
Other values (137)1293
42.9%
2025-11-10T21:23:56.286948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
07912
37.4%
/3016
 
14.3%
22289
 
10.8%
11660
 
7.9%
1508
 
7.1%
:1508
 
7.1%
4931
 
4.4%
3845
 
4.0%
5565
 
2.7%
8271
 
1.3%
Other values (3)623
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15096
71.5%
Other Punctuation4524
 
21.4%
Space Separator1508
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07912
52.4%
22289
 
15.2%
11660
 
11.0%
4931
 
6.2%
3845
 
5.6%
5565
 
3.7%
8271
 
1.8%
7261
 
1.7%
9200
 
1.3%
6162
 
1.1%
Other Punctuation
ValueCountFrequency (%)
/3016
66.7%
:1508
33.3%
Space Separator
ValueCountFrequency (%)
1508
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21128
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07912
37.4%
/3016
 
14.3%
22289
 
10.8%
11660
 
7.9%
1508
 
7.1%
:1508
 
7.1%
4931
 
4.4%
3845
 
4.0%
5565
 
2.7%
8271
 
1.3%
Other values (3)623
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII21128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07912
37.4%
/3016
 
14.3%
22289
 
10.8%
11660
 
7.9%
1508
 
7.1%
:1508
 
7.1%
4931
 
4.4%
3845
 
4.0%
5565
 
2.7%
8271
 
1.3%
Other values (3)623
 
2.9%

STATUS
Text

Missing 

Distinct6
Distinct (%)0.4%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:23:56.418415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.078912467
Min length7

Characters and Unicode

Total characters10675
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShipped
2nd rowShipped
3rd rowShipped
4th rowShipped
5th rowShipped
ValueCountFrequency (%)
shipped1399
90.0%
on38
 
2.4%
hold38
 
2.4%
cancelled30
 
1.9%
resolved29
 
1.9%
in9
 
0.6%
process9
 
0.6%
disputed3
 
0.2%
2025-11-10T21:23:56.649370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p2801
26.2%
e1529
14.3%
d1499
14.0%
i1402
13.1%
S1399
13.1%
h1399
13.1%
l127
 
1.2%
n77
 
0.7%
o76
 
0.7%
s50
 
0.5%
Other values (14)316
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9073
85.0%
Uppercase Letter1555
 
14.6%
Space Separator47
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p2801
30.9%
e1529
16.9%
d1499
16.5%
i1402
15.5%
h1399
15.4%
l127
 
1.4%
n77
 
0.8%
o76
 
0.8%
s50
 
0.6%
c39
 
0.4%
Other values (5)74
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S1399
90.0%
O38
 
2.4%
H38
 
2.4%
C30
 
1.9%
R29
 
1.9%
I9
 
0.6%
P9
 
0.6%
D3
 
0.2%
Space Separator
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10628
99.6%
Common47
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
p2801
26.4%
e1529
14.4%
d1499
14.1%
i1402
13.2%
S1399
13.2%
h1399
13.2%
l127
 
1.2%
n77
 
0.7%
o76
 
0.7%
s50
 
0.5%
Other values (13)269
 
2.5%
Common
ValueCountFrequency (%)
47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p2801
26.2%
e1529
14.3%
d1499
14.0%
i1402
13.1%
S1399
13.1%
h1399
13.1%
l127
 
1.2%
n77
 
0.7%
o76
 
0.7%
s50
 
0.5%
Other values (14)316
 
3.0%

QTR_ID
Real number (ℝ)

Missing 

Distinct4
Distinct (%)0.3%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean2.777851459
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:56.729125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.176122835
Coefficient of variation (CV)0.42339299
Kurtosis-1.393850796
Mean2.777851459
Median Absolute Deviation (MAD)1
Skewness-0.3492826553
Sum4189
Variance1.383264923
MonotonicityNot monotonic
2025-11-10T21:23:56.811857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
4591
20.9%
1321
 
11.4%
3312
 
11.1%
2284
 
10.1%
(Missing)1315
46.6%
ValueCountFrequency (%)
1321
11.4%
2284
10.1%
3312
11.1%
4591
20.9%
ValueCountFrequency (%)
4591
20.9%
3312
11.1%
2284
10.1%
1321
11.4%

MONTH_ID
Real number (ℝ)

Missing 

Distinct12
Distinct (%)0.8%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean7.305702918
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:56.899073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.502588952
Coefficient of variation (CV)0.479432163
Kurtosis-1.289794045
Mean7.305702918
Median Absolute Deviation (MAD)3
Skewness-0.3210388187
Sum11017
Variance12.26812937
MonotonicityNot monotonic
2025-11-10T21:23:56.993896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11314
 
11.1%
10173
 
6.1%
3133
 
4.7%
5132
 
4.7%
8129
 
4.6%
2111
 
3.9%
12104
 
3.7%
7102
 
3.6%
981
 
2.9%
480
 
2.8%
Other values (2)149
 
5.3%
(Missing)1315
46.6%
ValueCountFrequency (%)
177
2.7%
2111
3.9%
3133
4.7%
480
2.8%
5132
4.7%
ValueCountFrequency (%)
12104
 
3.7%
11314
11.1%
10173
6.1%
981
 
2.9%
8129
4.6%

YEAR_ID
Real number (ℝ)

Missing 

Distinct3
Distinct (%)0.2%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean2003.801724
Minimum2003
Maximum2005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:57.075125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12003
median2004
Q32004
95-th percentile2005
Maximum2005
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6987122088
Coefficient of variation (CV)0.0003486932865
Kurtosis-0.9419750236
Mean2003.801724
Median Absolute Deviation (MAD)1
Skewness0.2929233986
Sum3021733
Variance0.4881987507
MonotonicityNot monotonic
2025-11-10T21:23:57.173118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2004713
25.3%
2003547
19.4%
2005248
 
8.8%
(Missing)1315
46.6%
ValueCountFrequency (%)
2003547
19.4%
2004713
25.3%
2005248
 
8.8%
ValueCountFrequency (%)
2005248
 
8.8%
2004713
25.3%
2003547
19.4%

PRODUCTLINE
Text

Missing 

Distinct7
Distinct (%)0.5%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:23:57.306091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length12
Mean length10.8938992
Min length5

Characters and Unicode

Total characters16428
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMotorcycles
2nd rowMotorcycles
3rd rowMotorcycles
4th rowMotorcycles
5th rowMotorcycles
ValueCountFrequency (%)
cars821
30.9%
classic485
18.2%
vintage336
12.6%
motorcycles187
 
7.0%
trucks165
 
6.2%
and165
 
6.2%
buses165
 
6.2%
planes164
 
6.2%
ships131
 
4.9%
trains40
 
1.5%
2025-11-10T21:23:57.531268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s2808
17.1%
a2011
12.2%
C1306
 
7.9%
r1213
 
7.4%
1151
 
7.0%
c1024
 
6.2%
i992
 
6.0%
e852
 
5.2%
l836
 
5.1%
n705
 
4.3%
Other values (15)3530
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12783
77.8%
Uppercase Letter2494
 
15.2%
Space Separator1151
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s2808
22.0%
a2011
15.7%
r1213
9.5%
c1024
 
8.0%
i992
 
7.8%
e852
 
6.7%
l836
 
6.5%
n705
 
5.5%
t523
 
4.1%
o374
 
2.9%
Other values (7)1445
11.3%
Uppercase Letter
ValueCountFrequency (%)
C1306
52.4%
V336
 
13.5%
T205
 
8.2%
M187
 
7.5%
B165
 
6.6%
P164
 
6.6%
S131
 
5.3%
Space Separator
ValueCountFrequency (%)
1151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15277
93.0%
Common1151
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s2808
18.4%
a2011
13.2%
C1306
8.5%
r1213
7.9%
c1024
 
6.7%
i992
 
6.5%
e852
 
5.6%
l836
 
5.5%
n705
 
4.6%
t523
 
3.4%
Other values (14)3007
19.7%
Common
ValueCountFrequency (%)
1151
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s2808
17.1%
a2011
12.2%
C1306
 
7.9%
r1213
 
7.4%
1151
 
7.0%
c1024
 
6.2%
i992
 
6.0%
e852
 
5.2%
l836
 
5.1%
n705
 
4.3%
Other values (15)3530
21.5%

MSRP
Real number (ℝ)

Missing 

Distinct80
Distinct (%)5.3%
Missing1315
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean101.1047745
Minimum33
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.2 KiB
2025-11-10T21:23:57.653882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile43
Q168
median99
Q3124
95-th percentile170
Maximum214
Range181
Interquartile range (IQR)56

Descriptive statistics

Standard deviation40.0180736
Coefficient of variation (CV)0.3958079506
Kurtosis-0.05987990995
Mean101.1047745
Median Absolute Deviation (MAD)28
Skewness0.5918132432
Sum152466
Variance1601.446215
MonotonicityNot monotonic
2025-11-10T21:23:57.821819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9959
 
2.1%
11858
 
2.1%
13648
 
1.7%
6241
 
1.5%
6041
 
1.5%
6840
 
1.4%
8033
 
1.2%
8631
 
1.1%
10230
 
1.1%
5430
 
1.1%
Other values (70)1097
38.9%
(Missing)1315
46.6%
ValueCountFrequency (%)
3313
0.5%
3515
0.5%
3712
0.4%
4012
0.4%
4113
0.5%
ValueCountFrequency (%)
21416
0.6%
20716
0.6%
19411
0.4%
19315
0.5%
17316
0.6%

PRODUCTCODE
Text

Missing 

Distinct109
Distinct (%)7.2%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:23:58.187655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.115384615
Min length8

Characters and Unicode

Total characters12238
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS10_1678
2nd rowS10_1678
3rd rowS10_1678
4th rowS10_1678
5th rowS10_1678
ValueCountFrequency (%)
s18_323221
 
1.4%
s12_282318
 
1.2%
s18_332018
 
1.2%
s18_295718
 
1.2%
s10_475718
 
1.2%
s24_425818
 
1.2%
s18_313617
 
1.1%
s32_126817
 
1.1%
s700_193817
 
1.1%
s12_389116
 
1.1%
Other values (99)1330
88.2%
2025-11-10T21:23:58.875902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21691
13.8%
11538
12.6%
_1508
12.3%
S1508
12.3%
41067
8.7%
81064
8.7%
0991
8.1%
3900
7.4%
7575
 
4.7%
9504
 
4.1%
Other values (2)892
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9222
75.4%
Connector Punctuation1508
 
12.3%
Uppercase Letter1508
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21691
18.3%
11538
16.7%
41067
11.6%
81064
11.5%
0991
10.7%
3900
9.8%
7575
 
6.2%
9504
 
5.5%
6493
 
5.3%
5399
 
4.3%
Connector Punctuation
ValueCountFrequency (%)
_1508
100.0%
Uppercase Letter
ValueCountFrequency (%)
S1508
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10730
87.7%
Latin1508
 
12.3%

Most frequent character per script

Common
ValueCountFrequency (%)
21691
15.8%
11538
14.3%
_1508
14.1%
41067
9.9%
81064
9.9%
0991
9.2%
3900
8.4%
7575
 
5.4%
9504
 
4.7%
6493
 
4.6%
Latin
ValueCountFrequency (%)
S1508
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21691
13.8%
11538
12.6%
_1508
12.3%
S1508
12.3%
41067
8.7%
81064
8.7%
0991
8.1%
3900
7.4%
7575
 
4.7%
9504
 
4.1%
Other values (2)892
7.3%

CUSTOMERNAME
Text

Missing 

Distinct51
Distinct (%)3.4%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:23:59.151620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length25
Mean length20.55371353
Min length10

Characters and Unicode

Total characters30995
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLand of Toys Inc.
2nd rowReims Collectables
3rd rowLyon Souveniers
4th rowToys4GrownUps.com
5th rowCorporate Gift Ideas Co.
ValueCountFrequency (%)
mini349
 
7.8%
inc335
 
7.4%
gifts304
 
6.8%
co263
 
5.8%
ltd230
 
5.1%
collectables210
 
4.7%
distributors203
 
4.5%
gift174
 
3.9%
ideas98
 
2.2%
diecast83
 
1.8%
Other values (72)2248
50.0%
2025-11-10T21:23:59.536865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2989
 
9.6%
i2712
 
8.7%
s2345
 
7.6%
e2119
 
6.8%
t2092
 
6.7%
o1898
 
6.1%
n1668
 
5.4%
a1524
 
4.9%
l1383
 
4.5%
c1242
 
4.0%
Other values (42)11023
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22349
72.1%
Uppercase Letter4539
 
14.6%
Space Separator2989
 
9.6%
Other Punctuation1054
 
3.4%
Decimal Number56
 
0.2%
Dash Punctuation8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2712
12.1%
s2345
10.5%
e2119
9.5%
t2092
9.4%
o1898
8.5%
n1668
 
7.5%
a1524
 
6.8%
l1383
 
6.2%
c1242
 
5.6%
r1180
 
5.3%
Other values (15)4186
18.7%
Uppercase Letter
ValueCountFrequency (%)
C819
18.0%
M562
12.4%
G560
12.3%
I527
11.6%
L382
8.4%
D372
8.2%
S366
8.1%
T177
 
3.9%
A146
 
3.2%
W125
 
2.8%
Other values (11)503
11.1%
Other Punctuation
ValueCountFrequency (%)
.897
85.1%
&91
 
8.6%
'66
 
6.3%
Space Separator
ValueCountFrequency (%)
2989
100.0%
Decimal Number
ValueCountFrequency (%)
456
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin26888
86.7%
Common4107
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2712
 
10.1%
s2345
 
8.7%
e2119
 
7.9%
t2092
 
7.8%
o1898
 
7.1%
n1668
 
6.2%
a1524
 
5.7%
l1383
 
5.1%
c1242
 
4.6%
r1180
 
4.4%
Other values (36)8725
32.4%
Common
ValueCountFrequency (%)
2989
72.8%
.897
 
21.8%
&91
 
2.2%
'66
 
1.6%
456
 
1.4%
-8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2989
 
9.6%
i2712
 
8.7%
s2345
 
7.6%
e2119
 
6.8%
t2092
 
6.7%
o1898
 
6.1%
n1668
 
5.4%
a1524
 
4.9%
l1383
 
4.5%
c1242
 
4.0%
Other values (42)11023
35.6%

PHONE
Text

Missing 

Distinct50
Distinct (%)3.3%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:23:59.792837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length10
Mean length10.47214854
Min length9

Characters and Unicode

Total characters15792
Distinct characters16
Distinct categories7 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2125557818
2nd row26.47.1555
3rd row+33 1 46 62 7555
4th row6265557265
5th row6505551386
ValueCountFrequency (%)
4155551450180
 
8.8%
355563
 
3.1%
617555855551
 
2.5%
212555781849
 
2.4%
212555741348
 
2.3%
035-64055548
 
2.3%
650555138641
 
2.0%
26.47.155541
 
2.0%
408555365940
 
1.9%
6562-955540
 
1.9%
Other values (59)1453
70.7%
2025-11-10T21:24:00.185540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56379
40.4%
11362
 
8.6%
61089
 
6.9%
21063
 
6.7%
01012
 
6.4%
4972
 
6.2%
8811
 
5.1%
7708
 
4.5%
3680
 
4.3%
546
 
3.5%
Other values (6)1170
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14544
92.1%
Space Separator546
 
3.5%
Dash Punctuation308
 
2.0%
Other Punctuation122
 
0.8%
Open Punctuation95
 
0.6%
Close Punctuation95
 
0.6%
Math Symbol82
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
56379
43.9%
11362
 
9.4%
61089
 
7.5%
21063
 
7.3%
01012
 
7.0%
4972
 
6.7%
8811
 
5.6%
7708
 
4.9%
3680
 
4.7%
9468
 
3.2%
Space Separator
ValueCountFrequency (%)
546
100.0%
Dash Punctuation
ValueCountFrequency (%)
-308
100.0%
Other Punctuation
ValueCountFrequency (%)
.122
100.0%
Open Punctuation
ValueCountFrequency (%)
(95
100.0%
Close Punctuation
ValueCountFrequency (%)
)95
100.0%
Math Symbol
ValueCountFrequency (%)
+82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
56379
40.4%
11362
 
8.6%
61089
 
6.9%
21063
 
6.7%
01012
 
6.4%
4972
 
6.2%
8811
 
5.1%
7708
 
4.5%
3680
 
4.3%
546
 
3.5%
Other values (6)1170
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56379
40.4%
11362
 
8.6%
61089
 
6.9%
21063
 
6.7%
01012
 
6.4%
4972
 
6.2%
8811
 
5.1%
7708
 
4.5%
3680
 
4.3%
546
 
3.5%
Other values (6)1170
 
7.4%

ADDRESSLINE1
Text

Missing 

Distinct51
Distinct (%)3.4%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:24:00.500401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length42
Median length24
Mean length18.48806366
Min length11

Characters and Unicode

Total characters27880
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row897 Long Airport Avenue
2nd row59 rue de l'Abbaye
3rd row27 rue du Colonel Pierre Avia
4th row78934 Hillside Dr.
5th row7734 Strong St.
ValueCountFrequency (%)
st372
 
7.3%
strong250
 
4.9%
5677180
 
3.5%
circle135
 
2.6%
furth135
 
2.6%
dr133
 
2.6%
rue128
 
2.5%
street126
 
2.5%
avenue83
 
1.6%
ln83
 
1.6%
Other values (105)3476
68.1%
2025-11-10T21:24:00.957167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3593
 
12.9%
e1874
 
6.7%
t1680
 
6.0%
r1633
 
5.9%
n1386
 
5.0%
o1322
 
4.7%
i996
 
3.6%
a984
 
3.5%
S975
 
3.5%
l930
 
3.3%
Other values (47)12507
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14835
53.2%
Decimal Number5137
 
18.4%
Space Separator3593
 
12.9%
Uppercase Letter3346
 
12.0%
Other Punctuation836
 
3.0%
Dash Punctuation133
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1874
12.6%
t1680
11.3%
r1633
11.0%
n1386
9.3%
o1322
8.9%
i996
 
6.7%
a984
 
6.6%
l930
 
6.3%
g713
 
4.8%
u563
 
3.8%
Other values (11)2754
18.6%
Uppercase Letter
ValueCountFrequency (%)
S975
29.1%
A308
 
9.2%
L292
 
8.7%
C269
 
8.0%
P211
 
6.3%
D183
 
5.5%
F161
 
4.8%
V146
 
4.4%
M121
 
3.6%
R107
 
3.2%
Other values (10)573
17.1%
Decimal Number
ValueCountFrequency (%)
7874
17.0%
5713
13.9%
4552
10.7%
3529
10.3%
6507
9.9%
2486
9.5%
8443
8.6%
1358
7.0%
9353
6.9%
0322
 
6.3%
Other Punctuation
ValueCountFrequency (%)
.695
83.1%
'77
 
9.2%
?38
 
4.5%
#26
 
3.1%
Space Separator
ValueCountFrequency (%)
3593
100.0%
Dash Punctuation
ValueCountFrequency (%)
-133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18181
65.2%
Common9699
34.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1874
 
10.3%
t1680
 
9.2%
r1633
 
9.0%
n1386
 
7.6%
o1322
 
7.3%
i996
 
5.5%
a984
 
5.4%
S975
 
5.4%
l930
 
5.1%
g713
 
3.9%
Other values (31)5688
31.3%
Common
ValueCountFrequency (%)
3593
37.0%
7874
 
9.0%
5713
 
7.4%
.695
 
7.2%
4552
 
5.7%
3529
 
5.5%
6507
 
5.2%
2486
 
5.0%
8443
 
4.6%
1358
 
3.7%
Other values (6)949
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII27880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3593
 
12.9%
e1874
 
6.7%
t1680
 
6.0%
r1633
 
5.9%
n1386
 
5.0%
o1322
 
4.7%
i996
 
3.6%
a984
 
3.5%
S975
 
3.5%
l930
 
3.3%
Other values (47)12507
44.9%

ADDRESSLINE2
Text

Missing 

Distinct5
Distinct (%)3.6%
Missing2684
Missing (%)95.1%
Memory size22.2 KiB
2025-11-10T21:24:01.077766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1251
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuite 101
2nd rowSuite 750
3rd rowSuite 101
4th rowSuite 750
5th row2nd Floor
ValueCountFrequency (%)
suite103
37.1%
40048
17.3%
2nd36
 
12.9%
floor36
 
12.9%
10125
 
9.0%
75020
 
7.2%
20010
 
3.6%
2025-11-10T21:24:01.295291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0161
12.9%
139
11.1%
u103
 
8.2%
S103
 
8.2%
t103
 
8.2%
i103
 
8.2%
e103
 
8.2%
o72
 
5.8%
150
 
4.0%
448
 
3.8%
Other values (8)266
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter628
50.2%
Decimal Number345
27.6%
Space Separator139
 
11.1%
Uppercase Letter139
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u103
16.4%
t103
16.4%
i103
16.4%
e103
16.4%
o72
11.5%
n36
 
5.7%
d36
 
5.7%
l36
 
5.7%
r36
 
5.7%
Decimal Number
ValueCountFrequency (%)
0161
46.7%
150
 
14.5%
448
 
13.9%
246
 
13.3%
720
 
5.8%
520
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
S103
74.1%
F36
 
25.9%
Space Separator
ValueCountFrequency (%)
139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin767
61.3%
Common484
38.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
u103
13.4%
S103
13.4%
t103
13.4%
i103
13.4%
e103
13.4%
o72
9.4%
n36
 
4.7%
F36
 
4.7%
d36
 
4.7%
l36
 
4.7%
Common
ValueCountFrequency (%)
0161
33.3%
139
28.7%
150
 
10.3%
448
 
9.9%
246
 
9.5%
720
 
4.1%
520
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0161
12.9%
139
11.1%
u103
 
8.2%
S103
 
8.2%
t103
 
8.2%
i103
 
8.2%
e103
 
8.2%
o72
 
5.8%
150
 
4.0%
448
 
3.8%
Other values (8)266
21.3%

CITY
Text

Missing 

Distinct40
Distinct (%)2.7%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:24:01.481567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8.167771883
Min length3

Characters and Unicode

Total characters12317
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNYC
2nd rowReims
3rd rowParis
4th rowPasadena
5th rowSan Francisco
ValueCountFrequency (%)
san307
 
15.3%
rafael180
 
8.9%
nyc152
 
7.6%
new78
 
3.9%
francisco62
 
3.1%
bedford61
 
3.0%
bergamo48
 
2.4%
brickhaven47
 
2.3%
boston44
 
2.2%
reims41
 
2.0%
Other values (36)993
49.3%
2025-11-10T21:24:01.927813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1703
 
13.8%
e1161
 
9.4%
n889
 
7.2%
r692
 
5.6%
o666
 
5.4%
i636
 
5.2%
l563
 
4.6%
s506
 
4.1%
505
 
4.1%
S415
 
3.4%
Other values (35)4581
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9495
77.1%
Uppercase Letter2317
 
18.8%
Space Separator505
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1703
17.9%
e1161
12.2%
n889
9.4%
r692
 
7.3%
o666
 
7.0%
i636
 
6.7%
l563
 
5.9%
s506
 
5.3%
g353
 
3.7%
d260
 
2.7%
Other values (14)2066
21.8%
Uppercase Letter
ValueCountFrequency (%)
S415
17.9%
B350
15.1%
N285
12.3%
R260
11.2%
C242
10.4%
Y152
 
6.6%
P99
 
4.3%
F62
 
2.7%
A58
 
2.5%
V51
 
2.2%
Other values (10)343
14.8%
Space Separator
ValueCountFrequency (%)
505
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11812
95.9%
Common505
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1703
14.4%
e1161
 
9.8%
n889
 
7.5%
r692
 
5.9%
o666
 
5.6%
i636
 
5.4%
l563
 
4.8%
s506
 
4.3%
S415
 
3.5%
g353
 
3.0%
Other values (34)4228
35.8%
Common
ValueCountFrequency (%)
505
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1703
 
13.8%
e1161
 
9.4%
n889
 
7.2%
r692
 
5.6%
o666
 
5.4%
i636
 
5.2%
l563
 
4.6%
s506
 
4.1%
505
 
4.1%
S415
 
3.4%
Other values (35)4581
37.2%

STATE
Text

Missing 

Distinct11
Distinct (%)1.1%
Missing1784
Missing (%)63.2%
Memory size22.2 KiB
2025-11-10T21:24:02.093769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length2
Mean length2.359961501
Min length2

Characters and Unicode

Total characters2452
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca402
36.8%
ma190
17.4%
ny178
16.3%
ct61
 
5.6%
pa54
 
4.9%
nh34
 
3.1%
nv29
 
2.7%
isle26
 
2.4%
of26
 
2.4%
wight26
 
2.4%
Other values (3)65
 
6.0%
2025-11-10T21:24:02.390686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A646
26.3%
C485
19.8%
N262
10.7%
M190
 
7.7%
Y178
 
7.3%
e70
 
2.9%
T61
 
2.5%
P54
 
2.2%
52
 
2.1%
H34
 
1.4%
Other values (17)420
17.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2056
83.8%
Lowercase Letter344
 
14.0%
Space Separator52
 
2.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A646
31.4%
C485
23.6%
N262
12.7%
M190
 
9.2%
Y178
 
8.7%
T61
 
3.0%
P54
 
2.6%
H34
 
1.7%
V29
 
1.4%
I26
 
1.3%
Other values (4)91
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
e70
20.3%
s26
 
7.6%
l26
 
7.6%
o26
 
7.6%
f26
 
7.6%
i26
 
7.6%
g26
 
7.6%
h26
 
7.6%
t26
 
7.6%
u22
 
6.4%
Other values (2)44
12.8%
Space Separator
ValueCountFrequency (%)
52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2400
97.9%
Common52
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A646
26.9%
C485
20.2%
N262
10.9%
M190
 
7.9%
Y178
 
7.4%
e70
 
2.9%
T61
 
2.5%
P54
 
2.2%
H34
 
1.4%
V29
 
1.2%
Other values (16)391
16.3%
Common
ValueCountFrequency (%)
52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A646
26.3%
C485
19.8%
N262
10.7%
M190
 
7.7%
Y178
 
7.3%
e70
 
2.9%
T61
 
2.5%
P54
 
2.2%
52
 
2.1%
H34
 
1.4%
Other values (17)420
17.1%

POSTALCODE
Text

Missing 

Distinct36
Distinct (%)2.5%
Missing1377
Missing (%)48.8%
Memory size22.2 KiB
2025-11-10T21:24:02.649876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.143153527
Min length4

Characters and Unicode

Total characters7437
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10022
2nd row51100
3rd row75508
4th row90003
5th row94217
ValueCountFrequency (%)
97562205
 
13.0%
10022152
 
9.6%
9421789
 
5.6%
5055361
 
3.9%
2410048
 
3.0%
5833947
 
3.0%
5100344
 
2.8%
5110041
 
2.6%
502040
 
2.5%
4210039
 
2.5%
Other values (31)814
51.5%
2025-11-10T21:24:03.146490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01454
19.6%
21023
13.8%
1976
13.1%
5730
9.8%
7673
9.0%
9539
 
7.2%
4454
 
6.1%
3430
 
5.8%
6392
 
5.3%
8218
 
2.9%
Other values (13)548
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6889
92.6%
Uppercase Letter351
 
4.7%
Space Separator134
 
1.8%
Dash Punctuation63
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P52
14.8%
M52
14.8%
J48
13.7%
S38
10.8%
O26
7.4%
B25
7.1%
H22
6.3%
C22
6.3%
V22
6.3%
F22
6.3%
Decimal Number
ValueCountFrequency (%)
01454
21.1%
21023
14.8%
1976
14.2%
5730
10.6%
7673
9.8%
9539
 
7.8%
4454
 
6.6%
3430
 
6.2%
6392
 
5.7%
8218
 
3.2%
Space Separator
ValueCountFrequency (%)
134
100.0%
Dash Punctuation
ValueCountFrequency (%)
-63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7086
95.3%
Latin351
 
4.7%

Most frequent character per script

Common
ValueCountFrequency (%)
01454
20.5%
21023
14.4%
1976
13.8%
5730
10.3%
7673
9.5%
9539
 
7.6%
4454
 
6.4%
3430
 
6.1%
6392
 
5.5%
8218
 
3.1%
Other values (2)197
 
2.8%
Latin
ValueCountFrequency (%)
P52
14.8%
M52
14.8%
J48
13.7%
S38
10.8%
O26
7.4%
B25
7.1%
H22
6.3%
C22
6.3%
V22
6.3%
F22
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01454
19.6%
21023
13.8%
1976
13.1%
5730
9.8%
7673
9.0%
9539
 
7.2%
4454
 
6.1%
3430
 
5.8%
6392
 
5.3%
8218
 
2.9%
Other values (13)548
 
7.4%

COUNTRY
Text

Missing 

Distinct12
Distinct (%)0.8%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:24:03.322352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length3
Mean length4.181034483
Min length2

Characters and Unicode

Total characters6305
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowFrance
3rd rowFrance
4th rowUSA
5th rowUSA
ValueCountFrequency (%)
usa969
64.3%
italy113
 
7.5%
france81
 
5.4%
denmark63
 
4.2%
austria55
 
3.6%
canada44
 
2.9%
sweden38
 
2.5%
singapore36
 
2.4%
norway32
 
2.1%
uk26
 
1.7%
Other values (2)51
 
3.4%
2025-11-10T21:24:03.633887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S1043
16.5%
A1024
16.2%
U995
15.8%
a512
 
8.1%
e307
 
4.9%
n288
 
4.6%
r267
 
4.2%
i194
 
3.1%
t168
 
2.7%
l164
 
2.6%
Other values (20)1343
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3472
55.1%
Lowercase Letter2833
44.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a512
18.1%
e307
10.8%
n288
10.2%
r267
9.4%
i194
 
6.8%
t168
 
5.9%
l164
 
5.8%
y145
 
5.1%
m88
 
3.1%
p88
 
3.1%
Other values (9)612
21.6%
Uppercase Letter
ValueCountFrequency (%)
S1043
30.0%
A1024
29.5%
U995
28.7%
I113
 
3.3%
F81
 
2.3%
D63
 
1.8%
C44
 
1.3%
N32
 
0.9%
K26
 
0.7%
P26
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin6305
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1043
16.5%
A1024
16.2%
U995
15.8%
a512
 
8.1%
e307
 
4.9%
n288
 
4.6%
r267
 
4.2%
i194
 
3.1%
t168
 
2.7%
l164
 
2.6%
Other values (20)1343
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1043
16.5%
A1024
16.2%
U995
15.8%
a512
 
8.1%
e307
 
4.9%
n288
 
4.6%
r267
 
4.2%
i194
 
3.1%
t168
 
2.7%
l164
 
2.6%
Other values (20)1343
21.3%

TERRITORY
Text

Missing 

Distinct3
Distinct (%)0.6%
Missing2328
Missing (%)82.5%
Memory size22.2 KiB
2025-11-10T21:24:03.787438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.052525253
Min length4

Characters and Unicode

Total characters2006
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMEA
2nd rowEMEA
3rd rowEMEA
4th rowEMEA
5th rowEMEA
ValueCountFrequency (%)
emea433
87.5%
apac36
 
7.3%
japan26
 
5.3%
2025-11-10T21:24:04.092431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E866
43.2%
A505
25.2%
M433
21.6%
a52
 
2.6%
P36
 
1.8%
C36
 
1.8%
J26
 
1.3%
p26
 
1.3%
n26
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1902
94.8%
Lowercase Letter104
 
5.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E866
45.5%
A505
26.6%
M433
22.8%
P36
 
1.9%
C36
 
1.9%
J26
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
a52
50.0%
p26
25.0%
n26
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2006
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E866
43.2%
A505
25.2%
M433
21.6%
a52
 
2.6%
P36
 
1.8%
C36
 
1.8%
J26
 
1.3%
p26
 
1.3%
n26
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E866
43.2%
A505
25.2%
M433
21.6%
a52
 
2.6%
P36
 
1.8%
C36
 
1.8%
J26
 
1.3%
p26
 
1.3%
n26
 
1.3%

CONTACTLASTNAME
Text

Missing 

Distinct37
Distinct (%)2.5%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:24:04.280417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.00464191
Min length2

Characters and Unicode

Total characters9055
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYu
2nd rowHenriot
3rd rowDa Cunha
4th rowYoung
5th rowBrown
ValueCountFrequency (%)
nelson204
 
13.4%
young115
 
7.5%
frick91
 
6.0%
yu80
 
5.2%
hernandez70
 
4.6%
brown62
 
4.1%
king54
 
3.5%
rovelli48
 
3.1%
henriot41
 
2.7%
pipps40
 
2.6%
Other values (28)723
47.3%
2025-11-10T21:24:04.575781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n1104
 
12.2%
o917
 
10.1%
e910
 
10.0%
r691
 
7.6%
s568
 
6.3%
i560
 
6.2%
l390
 
4.3%
u363
 
4.0%
a347
 
3.8%
t237
 
2.6%
Other values (31)2968
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7507
82.9%
Uppercase Letter1528
 
16.9%
Space Separator20
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1104
14.7%
o917
12.2%
e910
12.1%
r691
9.2%
s568
 
7.6%
i560
 
7.5%
l390
 
5.2%
u363
 
4.8%
a347
 
4.6%
t237
 
3.2%
Other values (14)1420
18.9%
Uppercase Letter
ValueCountFrequency (%)
Y221
14.5%
N204
13.4%
B161
10.5%
H145
9.5%
F131
8.6%
T129
8.4%
M92
 
6.0%
P76
 
5.0%
R68
 
4.5%
K64
 
4.2%
Other values (6)237
15.5%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9035
99.8%
Common20
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1104
 
12.2%
o917
 
10.1%
e910
 
10.1%
r691
 
7.6%
s568
 
6.3%
i560
 
6.2%
l390
 
4.3%
u363
 
4.0%
a347
 
3.8%
t237
 
2.6%
Other values (30)2948
32.6%
Common
ValueCountFrequency (%)
20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9055
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1104
 
12.2%
o917
 
10.1%
e910
 
10.0%
r691
 
7.6%
s568
 
6.3%
i560
 
6.2%
l390
 
4.3%
u363
 
4.0%
a347
 
3.8%
t237
 
2.6%
Other values (31)2968
32.8%

CONTACTFIRSTNAME
Text

Missing 

Distinct37
Distinct (%)2.5%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:24:04.762274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.483421751
Min length3

Characters and Unicode

Total characters8269
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKwai
2nd rowPaul
3rd rowDaniel
4th rowJulie
5th rowJulie
ValueCountFrequency (%)
valarie257
 
16.7%
julie117
 
7.6%
sue84
 
5.4%
juri60
 
3.9%
maria58
 
3.8%
kwai49
 
3.2%
jeff48
 
3.1%
giovanni48
 
3.1%
kyung42
 
2.7%
paul41
 
2.7%
Other values (28)739
47.9%
2025-11-10T21:24:05.070046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1142
13.8%
e1073
13.0%
i953
11.5%
l766
 
9.3%
r532
 
6.4%
n458
 
5.5%
u398
 
4.8%
o323
 
3.9%
J315
 
3.8%
V283
 
3.4%
Other values (26)2026
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6691
80.9%
Uppercase Letter1543
 
18.7%
Space Separator35
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1142
17.1%
e1073
16.0%
i953
14.2%
l766
11.4%
r532
8.0%
n458
6.8%
u398
 
5.9%
o323
 
4.8%
t229
 
3.4%
g132
 
2.0%
Other values (10)685
10.2%
Uppercase Letter
ValueCountFrequency (%)
J315
20.4%
V283
18.3%
M164
10.6%
S123
 
8.0%
K101
 
6.5%
P94
 
6.1%
W92
 
6.0%
G88
 
5.7%
A70
 
4.5%
C60
 
3.9%
Other values (5)153
9.9%
Space Separator
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8234
99.6%
Common35
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1142
13.9%
e1073
13.0%
i953
11.6%
l766
 
9.3%
r532
 
6.5%
n458
 
5.6%
u398
 
4.8%
o323
 
3.9%
J315
 
3.8%
V283
 
3.4%
Other values (25)1991
24.2%
Common
ValueCountFrequency (%)
35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1142
13.8%
e1073
13.0%
i953
11.5%
l766
 
9.3%
r532
 
6.4%
n458
 
5.5%
u398
 
4.8%
o323
 
3.9%
J315
 
3.8%
V283
 
3.4%
Other values (26)2026
24.5%

DEALSIZE
Text

Missing 

Distinct3
Distinct (%)0.2%
Missing1315
Missing (%)46.6%
Memory size22.2 KiB
2025-11-10T21:24:05.188261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.49403183
Min length5

Characters and Unicode

Total characters8285
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmall
2nd rowSmall
3rd rowMedium
4th rowMedium
5th rowMedium
ValueCountFrequency (%)
medium745
49.4%
small673
44.6%
large90
 
6.0%
2025-11-10T21:24:05.383296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m1418
17.1%
l1346
16.2%
e835
10.1%
a763
9.2%
i745
9.0%
d745
9.0%
M745
9.0%
u745
9.0%
S673
8.1%
L90
 
1.1%
Other values (2)180
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6777
81.8%
Uppercase Letter1508
 
18.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m1418
20.9%
l1346
19.9%
e835
12.3%
a763
11.3%
i745
11.0%
d745
11.0%
u745
11.0%
r90
 
1.3%
g90
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
M745
49.4%
S673
44.6%
L90
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8285
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m1418
17.1%
l1346
16.2%
e835
10.1%
a763
9.2%
i745
9.0%
d745
9.0%
M745
9.0%
u745
9.0%
S673
8.1%
L90
 
1.1%
Other values (2)180
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m1418
17.1%
l1346
16.2%
e835
10.1%
a763
9.2%
i745
9.0%
d745
9.0%
M745
9.0%
u745
9.0%
S673
8.1%
L90
 
1.1%
Other values (2)180
 
2.2%